The age verification is an important task in various context of applications like access control in spaces in hotels which are prohibited for children and teenagers, in dangerous spaces for children and in public area during a spread of virus among others. In fact, the age verification consists in classifying the face images into different age groups while dealing with the face appearance variation affected by occlusion, pose variation, low resolution, scale variation and illumination variation. This work introduced an access control application based on the age verification in an uncontrolled environment. In fact, we proposed a new two-level age classification method based on deep learning in order to classify the face images into eight age groups. Actually, the two-level classification strategy help reducing the confusion between the inter and intra age groups. Our experiments were performed on the multi-constrained Adience benchmark. The obtained results illustrate the effectiveness and robustness of the proposed age classification method in an uncontrolled environment.
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